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In conducting a literature review, this article explores changes to Adult Learning and Education (ALE) policies and related processes as well as the context within which those changes have taken place, so as to draw out comparative insights regarding the unequal distribution of participation in ALE. Emphasis is on an analysis of political priorities, political-administrative guidelines, and organisational and administrative dimensions of ALE in Finland, Korea and the United States. While country-specific differences are identified, several convergences are also of importance: a priority assigned to ALE as a means to support market-driven policy, namely to enhance competitiveness in the globalised market; legislative efforts to support the demands for developing a skilled and competent workforce in the labour market; decentralised organisational and administrative structures that relate to ALE; and a decreasing trend in publicly funded ALE.

Using comparative data, this article examines the level and distribution of participation in adult education (AE) opportunities among countries that participated in PIAAC (Programme for the International Assessment of Adult Competencies). It considers observed cross-country patterns in relation to some mechanisms that drive unequal chances to participate and to some policy issues that surround the provision, governance, and financing of different types of adult learning. It also explores recent policy developments relevant to AE in three selected countries (Korea, Norway, and Vietnam).

Understanding the effects of groundwater withdrawal on streamflow depletion is important for effectively managing water resources. The Soil and Water Assessment Tool (SWAT) model has a groundwater module to calculate the groundwater budget and groundwater discharge. However, the water pumped from the aquifer is not considered in the SWAT module that estimates groundwater discharge. Therefore, this module was modified to consider the impact of groundwater pumping on the changes in groundwater discharge in the Bokhacheon watershed, South Korea. The model's water transfer module was improved to allow water from the aquifer to be transferred to destination locations, such as residential, industrial, and agricultural lands. Using the modified SWAT, streamflow responses to groundwater extraction were simulated for 2011–2019. The groundwater withdrawal induced decreases of 14.6 and 24.2% in low and drought flows, respectively, at the watershed's outlet. The groundwater withdrawals decreased groundwater flow and total water yield by 23.5% and 9.8%, respectively, and increased surface flow, lateral flow, percolation, soil water, and evapotranspiration owing to the increased soil moisture resulting from the partial re-infiltration of the groundwater pumped for agricultural irrigation. The modified SWAT can effectively estimate streamflow depletion resulting from groundwater pumping without extensive hydrogeological input data and computational time. [ABSTRACT FROM AUTHOR]

Future long‐term rainfall forecasts are valuable for operating water supply facilities and managing unusual droughts. This study proposes a novel approach to forecast basin‐scale monthly rainfall from lagged global climate indices, antecedent historical rainfall data of a targeted basin, and forecasted rainfall data from a nearby basin using a data‐driven model. The approach is applied to the Han River basin and the Geum River basin, South Korea, for May and June, prone to drought occurrence. An artificial neural network (ANN), a widely used data‐driven model, was employed to build forecasting models for the study basins. Two types of ANN models were constructed: one uses predictors of the lagged climate indices and antecedent rainfall of a targeted basin that have been typically used in previous studies, and the other further uses the forecasted rainfall of an adjacent basin that was first attempted in this study by considering the strong concurrent relationship of monthly rainfall data between nearby basins. The optimal network architectures were determined through the Monte Carlo cross‐validation (MCCV) process in which repeated data subsampling for training datasets was carried out to reduce the output variance and obtain ensemble forecasts. The results show that the proposed ANN model in this study with input variables of the forecasted rainfall of the nearby basin and the lagged global climate indices and the past rainfall of the target basin provides better predictive performance than the model without using the adjacent basin's forecast rainfall. The categorical forecasting skill based on the proposed approach is good: the hit rates and Heidke skill scores ranged from 50.9 to 66.0% and 0.29 to 0.49, respectively. The results confirm that using rainfall forecasts of a nearby basin as an input variable can enhance the ANN model's ability to predict future monthly rainfall. [ABSTRACT FROM AUTHOR]

Boron neutron capture therapy (BNCT) has been attracting interest as a new radiation modality for cancer therapy because it can selectively destroy cancer cells while maintaining the healthy state of surrounding normal cells. Many experimental trials have demonstrated significant BNCT treatment efficacy using neutron beams from research reactors. However, nuclear reactor technology cannot be scaled to sites in hospitals delivering patient treatment. Therefore, compact accelerator-based neutron sources that could be installed in many hospitals are under development or have even been commissioned at many facilities around the world. In Korea, a radio-frequency (RF) linac-based BNCT (A-BNCT) facility is under development by DawonMedax (DM). It provides the highly efficient production of an epithermal neutron beam with an optimized neutron energy spectrum range of 0.1~10 keV. With a 2-mA 10-MeV proton beam from the accelerator, the irradiation port epithermal neutron flux is higher than 1 × 109 n/cm2⋅s. Comprehensive verification and validation of the system have been conducted with the measurement of both proton and neutron beam characteristics. Significant therapeutic effects from BNCT have been confirmed by DM in both in vitro and in vivo non-clinical trials. Further, during exposure to epithermal neutrons, all other unintended radiation is controlled to levels meeting International Atomic Energy Agency (IAEA) recommendations. Recently, the Korean FDA has accepted an investigational new drug (IND) and the first-in-human clinical trial of BNCT is now being prepared. This paper introduces the principles of BNCT and accelerator-based neutron sources for BNCT and reports the recent advances of DM A-BNCT facility which is the main part of this paper. [ABSTRACT FROM AUTHOR]

A sand dam is an old technology used to trap sand materials and store water in dry riverbeds. Besides the volume of sand stored, the water storage and supply capacity of the sand dam depends principally on material properties including hydraulic conductivity (K) and the specific yield (Sy). In this study, the water supply capacity of a sand dam, applied in South Korea with a modified scheme, was analyzed. Imported sand materials were placed in three layers, and perforated drain pipes were installed to abstract the water to a collection tank. Using a Modular Finite-Difference Groundwater Flow (MODFLOW) model, the drain pipe discharge related to aquifer properties and supply capacity of a sand dam was evaluated. Using the model, the productivity of the sand storage with several cases of inflow conditions was assessed. For the period from 9 March 2020 to 16 May 2022, the results of calculation of water supply for the installation and operation of new sand dam are as follows. Regarding the actual inflow condition, compared to the average water supply of the existing water intake source, the sand dam installation increased water supply by 61.6%, and the sand dam installation showed an effect of improving the water supply by 36.0% for the minimum water supply. The calculated water supply for the condition where the inflow was reduced by 50% showed an effect of improving water supply by over 70% on average due to the installation of the sand dam. The findings would be a benchmark for future expansions of the scheme in related places, and management and maintenance of the system. [ABSTRACT FROM AUTHOR]

Boron neutron capture therapy (BNCT) is a radiation therapy that selectively kills cancer cells and is being actively researched and developed around the world. In Korea, development of the proton linear accelerator-based BNCT system has completed development, and its anti-cancer effect in the U-87 MG subcutaneous xenograft model has been evaluated. To evaluate the efficacy of BNCT, we measured 10B-enriched boronophenylalanine (BPA) uptake in U-87 MG, FaDu, and SAS cells and evaluated cell viability by clonogenic assays. In addition, the boron concentration in the tumor, blood, and skin on the U-87 MG xenograft model was measured, and the tumor volume was measured for 4 weeks after BNCT. In vitro, the intracellular boron concentration was highest in the order of SAS, FaDu, and U-87 MG, and cell survival fractions decreased depending on the BPA treatment concentration and neutron irradiation dose. In vivo, the tumor volume was significantly decreased in the BNCT group compared to the control group. This study confirmed the anti-cancer effect of BNCT in the U-87 MG subcutaneous xenograft model. It is expected that the proton linear accelerator-based BNCT system developed in Korea will be a new option for radiation therapy for cancer treatment. [ABSTRACT FROM AUTHOR]

Highways, while vital for transportation, often lead to heightened air and noise pollution, adversely affecting nearby communities. This study delves into the effectiveness of highway capping, a sustainable urban development strategy, in addressing these environmental challenges, with a specific focus on the Bundang-Suseo Highway in South Korea. This study employed a multifaceted approach, incorporating on-road monitoring, in situ measurements, and vertical assessments using UAVs. Following the cap's installation, the area experienced more stable pollutant levels, marking a notable shift from the previously fluctuating conditions heavily influenced by the highway. In-depth in situ monitoring near the cap revealed significant reductions in noise and pollutants like UFP and BC. Furthermore, UAV monitoring captured these changes in pollutant levels at different altitudes. Notably, the installation of the highway cap led to increased PM 2.5 , PM 10 , and NO 2 levels at ground level, but a decrease above the cap, emphasizing the critical importance of intentional highway cap design in enhancing urban air quality and reducing exposure to harmful pollutants. This research yields invaluable insights for urban planners, health authorities, and policymakers, aiding the precise identification of pollution-prone areas and advocating for improved highway cap design to enhance urban environments. [Display omitted] • The study shows highway capping stabilizes urban PM levels. • Noise saw the most notable reduction after highway cap installation. • In-situ monitoring reveals reductions in local BC and UFP near the highway cap. • UAV monitoring shows ground-level rise in PM, NO 2 , but a decrease above the cap. • Highway cap structure crucially shapes pollutant levels and wind dynamics. [ABSTRACT FROM AUTHOR]

Despite the expanding recognition of the impact of urban design on the well-being of citizens in planned communities, air quality concerns are frequently neglected in the development of new towns. This study aimed to address this gap in the literature by conducting comparative field assessments of different street structures in South Korea's new towns, measuring the concentrations of ultrafine particles (UFP), fine particulate matter (PM2.5), and black carbon (BC) in street canyons with varying aspect ratios (ARs) and road configurations. During a six-day period in October, measurements were taken both before and after business hours in pedestrian zones with ARs of 2.5 and 10, as well as vehicle zones with open and loop roads. The findings revealed that the concentrations of UFP, PM2.5, and BC were consistently higher in a deeper canyon (AR 10) than in a shallower canyon (AR 2.5) in the pedestrian zone throughout the day, with the difference being most pronounced during periods of heightened commercial activity. Additionally, vehicle zones with loop roads had higher levels of air pollution, particularly in terms of UFP and PM2.5 concentrations, compared to open roads, regardless of vehicle traffic volume. The study highlights the importance of evaluating the influence of street configuration on air circulation, regulating emissions from direct sources, and adopting a comprehensive approach to urban planning to improve air quality and safeguard public health in densely populated areas. [Display omitted] • Street configuration comparisons examined new town's urban form—air pollution relationships. • The higher the AR of the pedestrianizing street canyon, the higher the concentrations of UFP, PM2.5, and BC. • Deeper canyon (AR 10) PM2.5 twice higher than shallower (AR 2.5) in vehicular-free zone, via mobile measurements. • Loop roads had consistently higher UFP, and PM2.5 concentrations than open roads. [ABSTRACT FROM AUTHOR]

• Diurnal patterns of pollutant concentrations were analyzed. • Different urban form factors contribute to the emission and dispersion of pollutants. • Our results showed that mixed land use areas had adverse impacts on air quality. • Highly polluted areas tended to have a consistent pollution level during the day. • Particulate concentrations alleviated in the evening and overnight. Despite the aggressive emission control efforts in Korea over the past decades, surface-level particulate matter (PM) concentrations have increased in Seoul since 2012. What remains unknown are the influences of urban form and their interactions with seasonal and cyclic changes. This study investigates the diurnal pattern of PM concentrations (PM2.5 and PM10) and their clustering patterns related to urban form factors in Seoul, Korea. Using data obtained from 802 environmental sensors distributed across the city from April 2020 to March 2021, we found the highest PM concentrations in high traffic–concentrated areas with high-rise buildings as well as areas characterized by high proximity to highways and high mixed land uses. Furthermore, diurnal differences in pollutant concentrations were more pronounced in high traffic–concentrated areas with high-rise buildings as opposed to the areas characterized by a larger fraction of residential apartment complexes. This study demonstrates that high-resolution environmental sensor data can provide more granular information regarding spatial distribution and diurnal patterns of PM, which can help inform more targeted intervention strategies. The findings also suggest that bundling of urban design strategies aimed at reducing traffic emissions while diluting traffic pollutants through dispersion could be effective in managing urban air quality problems in high-density cities. [ABSTRACT FROM AUTHOR]

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In this study, artificial neural network (ANN) models were constructed to predict the rainfall during May and June for the Han River basin, South Korea. This was achieved using the lagged global climate indices and historical rainfall data. Monte-Carlo cross-validation and aggregation (MCCVA) was applied to create an ensemble of forecasts. The input-output patterns were randomly divided into training, validation, and test datasets. This was done 100 times to achieve diverse data splitting. In each data splitting, ANN training was repeated 100 times using randomly assigned initial weight vectors of the network to construct 10,000 prediction ensembles and estimate their prediction uncertainty interval. The optimal ANN model that was used to forecast the monthly rainfall in May had 11 input variables of the lagged climate indices such as the Arctic Oscillation (AO), East Atlantic/Western Russia Pattern (EAWR), Polar/Eurasia Pattern (POL), Quasi-Biennial Oscillation (QBO), Sahel Precipitation Index (SPI), and Western Pacific Index (WP). The ensemble of the rainfall forecasts exhibited the values of the averaged root mean squared error (RMSE) of 27.4, 33.6, and 39.5 mm, and the averaged correlation coefficient (CC) of 0.809, 0.725, and 0.641 for the training, validation, and test sets, respectively. The estimated uncertainty band has covered 58.5% of observed rainfall data with an average band width of 50.0 mm, exhibiting acceptable results. The ANN forecasting model for June has 9 input variables, which differed from May, of the Atlantic Meridional Mode (AMM), East Pacific/North Pacific Oscillation (EPNP), North Atlantic Oscillation (NAO), Scandinavia Pattern (SCAND), Equatorial Eastern Pacific SLP (SLP_EEP), and POL. The averaged RMSE values are 39.5, 46.1, and 62.1 mm, and the averaged CC values are 0.853, 0.771, and 0.683 for the training, validation, and test sets, respectively. The estimated uncertainty band for June rainfall forecasts generally has a coverage of 67.9% with an average band width of 83.0 mm. It can be concluded that the neural network with MCCVA enables us to provide acceptable medium-term rainfall forecasts and define the prediction uncertainty interval. [ABSTRACT FROM AUTHOR]

In this study, long-term precipitation forecasting models capable of reflecting constantly changing climate characteristics and providing forecasts for up to 12 months in advance were developed using lagged correlations with global and local climate indices. These models were applied to predict monthly precipitation in the Han River basin, South Korea. Based on the lead month of forecast, 10 climate indices with high correlations were selected and combined to construct four-variable multiple regression models for monthly precipitation forecasting. The forecast results for the analytical period (2010–2019) showed that predictability was low for some summer seasons but satisfactory for other seasons and long periods. In the goodness-of-fit test results, the Nash–Sutcliffe efficiency (0.48–0.57) and the ratio of the root mean square error to the standard deviation of the observation (0.66–0.72) were evaluated to be satisfactory while the percent bias (9.4–15.5%) was evaluated to be between very good and good. Due to the nature of the statistical models, however, the predictability is highly likely to be reduced if climate phenomena that are different from the statistical characteristics of the past appear in the forecast targets or predictors. The forecast results were also presented as tercile probability information (below normal, normal, above normal) through a comparison with the observation data of the past 30 years. The results are expected to be utilized as useful forecast information in practice if the predictability for some periods is improved. [ABSTRACT FROM AUTHOR]

This study develops a late spring-early summer rainfall forecasting model using an artificial neural network (ANN) for the Geum River Basin in South Korea. After identifying the lagged correlation between climate indices and the rainfall amount in May and June, 11 significant input variables were selected for the preliminary ANN structure. From quantification of the relative importance of the input variables, the lagged climate indices of East Atlantic Pattern (EA), North Atlantic Oscillation (NAO), Pacific Decadal Oscillation (PDO), East Pacific/North Pacific Oscillation (EP/NP), and Tropical Northern Atlantic Index (TNA) were identified as significant predictors and were used to construct a much simpler ANN model. The final best ANN model, with five input variables, showed acceptable performance with relative root mean square errors of 25.84%, 32.72%, and 34.75% for training, validation, and testing data sets, respectively. The hit score, which is the number of hit years divided by the total number of years, was more than 60%, which indicates that the ANN model successfully predicts rainfall in the study area. The developed ANN model, incorporated with lagged global climate indices, could allow for more timely and flexible management of water resources and better preparation against potential droughts in the study region. [ABSTRACT FROM AUTHOR]

A proton linac based boron neutron capture therapy system (A-BNCT, 10MeV, 4mA) was successfully developed in Korea. We performed in vitro experiments with U87 and SAS cells and revealed the efficacy of a binary therapy BNCT using epithermal neutrons and boronophenylalanine (BPA). The results revealed that BNCT showed cancer cell selectivity and caused cell death. Further in vitro studies can be a valuable method to characterize an A-BNCT system. We expect BNCT to become a treatment option for cancer patients. • Proton Linac based BNCT (A-BNCT) has been developed in Korea. • Cell death was confirmed in U87 and SAS cells using an A-BNCT system. • We are performing preclinical trials and clinical trials with an A-BNCT system. [ABSTRACT FROM AUTHOR]