• Journal of the Korean Institute of Landscape Architecture
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• v.49 no.3
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• pp.11-28
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• 2021

Due to its size and complex characteristics, it is not often to newly create a large park within an existing urban area. Also, there has been a lack of research on the planting design methodologies for a large park. This study aims to elucidate how ecological ideas can be applied to planting practice from a designer's perspective, and eventually suggest a planting design framework in the actual case, the Central Park in the City of Gwangju. This framework consists of spatial structure of planting area in order to connect and unite the separated green patches, to adapt to the changes of existing vegetation patterns, to maintain the visual continuity of landscape, and to organize the whole open space system. The framework can be provided for the spatial planning and planting design phase in which the landscape designer flexibly uses it with the design intentions as well as with an understanding of the physical, social, and aesthetic characteristics of the site. The significance of this approach is, first that it can maintain ecological and visual consistency of the both existing and introduced landscapes as a whole in spite of its intrinsic complexity and largeness, and second that it can help efficiently respond to the unexpected changes in the landscape. In the case study, comprehensive site analysis is conducted before developing the framework. In particular, wetlands and grasslands have been identified as potential wildlife habitat which critically determines the vegetation patterns of the green area. Accordingly, the lists of plant communities are presented along with the planting scheme for their shape, layout, and relations. The model of the plant community is developed responding to the structure of surrounding natural landscape. However, it is not designed to evolve to a specific plant community, but is rather a conceptual model of ecological potentials. Therefore, the application of the model has great flexibility by using other plant communities as an alternative as long as the characteristics of the communities are appropriate to the physical conditions. Even though this research provides valuable implications for landscape planning and design in the similar circumstances, there are several limitations to be overcome in the further research. First, there needs to be more sufficient field surveys on the wildlife habitats, which would help generate a more concrete planting model. Second, a landscape management plan should be included considering the condition of existing forest, in particular the afforested landscapes. Last, there is a lack of quantitative data for the models of some plant communities.

• The Journal of Engineering Geology
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• v.30 no.4
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• pp.457-468
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• 2020

Problems arising during pile design works for plant construction, civil and architecture work are mostly come from uncertainty of geotechnical characteristics. In particular, obtaining the N-value measured through the Standard Penetration Test (SPT) is the most important data. However, it is difficult to obtain N-value by drilling investigation throughout the all target area. There are many constraints such as licensing, time, cost, equipment access and residential complaints etc. it is impossible to obtain geotechnical characteristics through drilling investigation within a short bidding period in overseas. The geotechnical characteristics at non-drilling investigation points are usually determined by the engineer's empirical judgment, which can leads to errors in pile design and quantity calculation causing construction delay and cost increase. It would be possible to overcome this problem if N-value could be predicted at the non-drilling investigation points using limited minimum drilling investigation data. This study was conducted to predicted the N-value using an Artificial Neural Network (ANN) which one of the Artificial intelligence (AI) method. An Artificial Neural Network treats a limited amount of geotechnical characteristics as a biological logic process, providing more reliable results for input variables. The purpose of this study is to predict N-value at the non-drilling investigation points through patterns which is studied by multi-layer perceptron and error back-propagation algorithms using the minimum geotechnical data. It has been reviewed the reliability of the values that predicted by AI method compared to the measured values, and we were able to confirm the high reliability as a result. To solving geotechnical uncertainty, we will perform sensitivity analysis of input variables to increase learning effect in next steps and it may need some technical update of program. We hope that our study will be helpful to design works in the future.

• Korean Journal of Agricultural and Forest Meteorology
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• v.23 no.4
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• pp.198-221
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• 2021

After large-scale reforestation in the 1960s and 1970s, forests in Korea have gradually been aging. Net ecosystem CO₂ exchange of old-growth forests is theoretically near zero; however, it can be a CO₂ sink or source depending on the intervention of disturbance or management. In this study, we report the CO₂ budget dynamics of the Gwangneung deciduous old-growth forest (GDK) in Korea and examined the following two questions: (1) is the preserved GDK indeed CO₂ neutral as theoretically known? and (2) can we explain the dynamics of CO₂ budget by the common mechanisms reported in the literature? To answer, we analyzed the 15-year long CO₂ flux data measured by eddy covariance technique along with other biometeorological data at the KoFlux GDK site from 2006 to 2020. The results showed that (1) GDK switched back-and-forth between sink and source of CO₂ but averaged to be a week CO₂ source (and turning to a moderate CO₂ source for the recent five years) and (2) the interannual variability of solar radiation, growing season length, and leaf area index showed a positive correlation with that of gross primary production (GPP) (R²=0.32~0.45); whereas the interannual variability of both air and surface temperature was not significantly correlated with that of ecosystem respiration (RE). Furthermore, the machine learning-based model trained using the dataset of early monitoring period (first 10 years) failed to reproduce the observed interannual variations of GPP and RE for the recent five years. Biomass data analysis suggests that carbon emissions from coarse woody debris may have contributed partly to the conversion to a moderate CO₂ source. To properly understand and interpret the long-term CO₂ budget dynamics of GDK, new framework of analysis and modeling based on complex systems science is needed. Also, it is important to maintain the flux monitoring and data quality along with the monitoring of coarse woody debris and disturbances.

• Korean Journal of Heritage: History & Science
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• v.55 no.2
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• pp.102-111
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• 2022

Termites are a group of social insects that are one of the primary causes of damage to wooden architectural heritage. Since termite damage impairs the authenticity and structural stability of cultural heritage, it is imperative to prevent it. This study examines the extent of termite damage to wooden architectural heritage as part of efforts to prevent termite damage to nationally designated wooden architectural heritage sites across the country. The extent of termite damage to each cultural heritage was assessed qualitatively and quantitatively and comparatively analyzed by region using the results of the "Investigation on Biological Damage to Wooden Architectural Heritages" conducted by the National Research Institute of Cultural Heritage from 2016 to 2019. It involved 362 nationally designated wooden architectural heritages(25 national treasures, 157 treasures, 180 national folklore cultural heritages) and 1,104 buildings. The results were as follows: termite detection dogs reacted at 317(87.6%) of the 362 wooden heritages, with visible termite damage observed in 185 cases(51.1%). Furthermore, termite damage was confirmed using one of two methods(detection dogs or visual inspection) in 324 cases(89.5%). Of the 1,104 buildings, termite detection dogs reacted at 668(60.5%), while 339(30.7%) showed visible termite damage. Employing one of the two methods, damage was confirmed in 702 buildings(63.6%). The country was categorized into nine regions(Seoul Metropolitan Area, Gangwon, Chungbuk, Chungnam, Jeonbuk, Jeonnam, Gyeongbuk, Gyeongnam, and Jeju) to examine the termite damage rate and the degree of damage to each cultural heritage according to location. Termite detection dogs reacted to more than 70% of the cultural heritage in all regions. Visible damage was minimal in the Seoul metropolitan area(32.1%) and Gangwon(21.4%) but severe in Chungnam(65.6%), Jeonnam(67.3%), and Gyeongnam(68.2%). By quantifying the degree of termite damage of each cultural heritage as a ratio of the absence of termite damage among the total absence, the average termite damage of the cultural heritage across the country was 9.2%. Regional variance analysis showed that the cultural heritage in Jeonbuk and Jeonnam showed a statistically significantly higher degree of termite damage than the cultural heritage in the Seoul metropolitan area, Chungbuk, and Gyeongbuk. This paper comprehensively analyzed termite damage to nationally designated wooden architectural heritage. The findings are expected to be valuable in establishing policies for the preservation and management of cultural heritage sites in the future.

• Korean Journal of Environment and Ecology
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• v.36 no.3
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• pp.327-337
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• 2022

Ecological restoration is considered a good means to prevent biodiversity loss in terms of the ecosystem's health and sustainability. However, there are difficulties in putting it into practice as there is no comprehensive and objective standard for the selection of plant species, such as environmental, ecological factors, and restoration goal setting. Therefore, this study developed an evaluation index necessary for selecting plant species for restoration using the Delphi method that synthesizes the opinions of the expert group. A survey with 38 questionnaires was conducted twice for experts in ecological restoration, etc., and the importance and priority of evaluation indicators were analyzed by dividing the restoration targets into inland and island regions. The result of the importance analysis showed that "native plants" had the highest average of 4.9 among the evaluation indices in both inland and island regions, followed by "seed security", "propagation", and "root growth rate". In the inland region, the index priority was analyzed in the order of "native plants", "appearance frequency", "root growth rate", "distribution range", and "seed security" in the island region, it was analyzed in the order of "native plants", "root growth rate", "appearance frequency", "distribution range", and "tolerance", showing slight differences between the two indicators. As a result of the importance and priority indicator analysis, we set the mean importance and priority of 4.1 and 2.9, respectively, in the inland region and 4.2 and 2.9, respectively, in the island region. As for the criteria of selecting plant species for ecological restoration, the "native plants" had the highest importance and priority. "Seed securing", 'viability", "topography", "proliferation", "tolerance", "soil conditions", "growth characteristics", "early succession", "distribution range", "appearance frequency", and "germination rate" were classified into subgroups of low importance and priority. The lowest indicators were "final stage of succession", "transition period", 'transition stage", "root", "reproduction", "soil", "appearance", "technology", "landscape", "climate", and "germination rate". We expected that the findings through objective verification in this study would be used as evaluation indicators for selecting native plant species for ecological restoration.

• Journal of The Korean Association For Science Education
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• v.43 no.6
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• pp.549-562
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• 2023

The current society is in the VUCA era, where various risks produced by humans are spread along with the development of science and technology. There is a need to increase the level of risk literacy of citizens to strengthen their daily preparedness to respond to these risks. For this on, it is necessary to reconsider the role of science education so that risks can be perceived and responded to scientifically and objectively. Accordingly, in order to investigate the role of science education in a risk society, this study reviewed the history of risk perception and response related to science and analyzed its characteristics. In this process, perception and response to risks arising from blood pressure were analyzed in three contexts (historical context, curriculum context, textbook context). For historical context, journals registered in SCIE were selected as research subjects among journals where research related to the history of knowledge of the heart and cardiovascular system was conducted. Papers with the keywords 'hypertension' and 'history' were selected from the journals, and changes in perception and responses related to blood pressure were compared and analyzed by period. The curriculum context is analyzed from the 1st national curriculum to the 2022 revised curriculum, and content elements and achievement standard related to blood pressure were compared and analyzed. It was confirmed that risks arising from blood pressure were not included from the 1st to the 6th national curriculum, and that risks arising from blood pressure were included from the 7th national curriculum (excluding the 2009 revised curriculum). For the textbook context, the 7th national curriculum BiologyⅠ, the 2015 revised curriculum Life ScienceⅠ, and Health were selected, and through text mining, keywords that representing curriculums and textbooks were selected, and the presentation of risk perception and response was analyzed based on the keywords. And by analyzing the figures and tables presented in the textbook, the characteristics of risk perception and risk response were derived. This study is meaningful in that it was able to confirm the role of risk perception and response in science education.

• Korean Journal of Agricultural and Forest Meteorology
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• v.25 no.4
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• pp.398-403
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• 2023

Process-based K-cabbage model is based on physiological processes such as photosynthesis and phenology, making it possible to predict crop growth under different climate conditions that have never been experienced before. Current first-stage process-based models can be used to assess climate impact through yield prediction based on climate change scenarios, but no comparison has been performed between big data obtained from the main production area and model prediction so far. The aim of this study was to find out the direction of model improvement when using the current model for yield prediction. For this purpose, model performance evaluation was conducted based on data collected from farmers growing 'Chungwang' cabbage in Taebaek and Samcheok, the main producing areas of Chinese cabbage in highland region. The farms surveyed in this study had different cultivation methods in terms of planting date and soil water and nutrient management. The results showed that the potential biomass estimated using the K-cabbage model exceeded the observed values in all cases. Although predictions and observations at the time of harvest did not show a complete positive correlation due to limitations caused by the use of fresh weight in the model evaluation process (R²=0.74, RMSE=866.4), when fitting the model based on the values 2 weeks before harvest, the growth suitability index was different for each farm. These results are suggested to be due to differences in soil properties and management practices between farms. Therefore, to predict attainable yields taking into account differences in soil and management practices between farms, it is necessary to integrate dynamic soil nutrient and moisture modules into crop models, rather than using arbitrary growth suitability indices in current K-cabbage model.

• Korean Journal of Remote Sensing
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• v.39 no.5_1
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• pp.669-681
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• 2023

Studies have tried to apply remote sensing technology, a non-destructive survey method, instead of the existing destructive survey, which requires relatively large labor input and a long time to estimate chlorophyll content, which is an important indicator for evaluating the growth of fruit trees. This study was conducted to non-destructively evaluate the chlorophyll content of pear tree leaves using unmanned aerial vehicle-based hyperspectral imagery for two years(2021, 2022). The reflectance of the single bands of the pear tree canopy extracted through image processing was band rationed to minimize unstable radiation effects depending on time changes. The estimation (calibration and validation) models were developed using machine learning algorithms of elastic-net, k-nearest neighbors(KNN), and support vector machine with band ratios as input variables. By comparing the performance of estimation models based on full band ratios, key band ratios that are advantageous for reducing computational costs and improving reproducibility were selected. As a result, for all machine learning models, when calibration of coefficient of determination (R²)≥0.67, root mean squared error (RMSE)≤1.22 ㎍/cm², relative error (RE)≤17.9% and validation of R²≥0.56, RMSE≤1.41 ㎍/cm², RE≤20.7% using full band ratios were compared, four key band ratios were selected. There was relatively no significant difference in validation performance between machine learning models. Therefore, the KNN model with the highest calibration performance was used as the standard, and its key band ratios were 710/714, 718/722, 754/758, and 758/762 nm. The performance of calibration showed R²=0.80, RMSE=0.94 ㎍/cm², RE=13.9%, and validation showed R²=0.57, RMSE=1.40 ㎍/cm², RE=20.5%. Although the performance results based on validation were not sufficient to estimate the chlorophyll content of pear tree leaves, it is meaningful that key band ratios were selected as a standard for future research. To improve estimation performance, it is necessary to continuously secure additional datasets and improve the estimation model by reproducing it in actual orchards. In future research, it is necessary to continuously secure additional datasets to improve estimation performance, verify the reliability of the selected key band ratios, and upgrade the estimation model to be reproducible in actual orchards.

• Korean Chemical Engineering Research
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• v.61 no.4
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• pp.555-560
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• 2023

Conventional aqueous amine-based CO₂ capture has a problem in that a large amount of renewable energy is required for CO₂ stripping and solvent regeneration in its industrial applications. This work proposes a water-lean absorbent that can reduce regeneration energy by lowering the water content in the absorbent with high absorption capacity for CO₂. To this purpose, this water-lean solvent introduced NMP (N-methyl-2-pyrrolidone), which has a higher physical solubility in CO₂ and a low specific heat capacity comparing to water, along with 3-methylaminopropylamine (MAPA), a diamine, into the absorbent. The circulating absorption capacity and absorption rate for CO₂ of this water-lean solvent were measured using a packed tower. When NMP was added to the absorbent, the absorption rate was improved. In the case of the absorbent containing 2.5M MAPA was used, the maximum circulating absorption capacity was obtained when 10 wt% of NMP was included in absorbent. The overall mass transfer coefficient increased as the concentration of NMP increased. However, at loading values higher than 0.5, the increment in mass transfer coefficient decreased as the concentration of NMP increased. When the lean loading value is low, the mass transfer resistance due to viscosity of the absorbent is low, so the overall mass transfer coefficient increases with the addition of NMP. However, as the lean loading value increases, the viscosity of the absorbent increases, and the diffusivity of CO₂ and MAPA decreases, resulting in sharply decreasing of the overall mass transfer coefficient.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.14 no.2
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• pp.69-79
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• 2009

Major and trace elements were analyzed in three core sediments to investigate geochemical characteristics of East Sea sediments and provenance changes during late Quaternary in Ulleung Basin. Comparing with Yellow and South Sea sediments, contents of major elements were generally similar while contents of trace elements were significantly different. Furthermore, within this basin, there were some variabilities in trace element compositions. In the western slope sediments (WS), Mo was enriched over 6 times as much as other sites. On the other hand, Zr, Nb, Hf and Ta were enriched in basin sediments (Basin), and Ca and Cs were enriched in southern slope sediments (SS). After excluding elements derived from biogenic, authigenic and diagenetic origins, the lithogenic elements (K, Ti, Cs, Zr, Nb, Hf and Ta) could be classified into three groups from the comparison of element/Al ratios among cores. The first group consisted of elements (K and Ti) that showed the nearly similar element/Al ratios among three cores. The second group contained Cs which showed significant difference between two slope sediments. The third group elements (Zr, Nb, Hf and Ta) showed highly enriched in basin relative to both slope areas. The depth profiles of metal/Al ratios in basin sediments provided the following interpretation for the compositions of sediment and their variation. From 10,000 yr B.P. to 7,000 yr B.P. two lithogenic components (volcanic ashes and western slope sediments) were mixed and deposited in the basin. After 7,000 yr B.P., however, southern slope sediments were mixed with volcanic ashes and deposited in basin area. This event of source change is nearly close to inflow period of the Tsushima Warm Current to Ulleung Basin. Thus, it might be suggested that element geochemistry in Ulleung basin sediment indicate the change of current system in the study area.