• Journal of Korean Society of Rural Planning
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• v.24 no.4
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• pp.69-80
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• 2018

In this study, we typified the project for revitalizing the rural area through text analysis using news data, and analyzed the main direction and characteristics of the project. In order to examine the factors emphasized among the issues related to the revitalization of rural areas, we used news data related to 'tourism' and 'livelihood', which are the main keyword of the project to promote rural areas. In the analysis, text mining techniques were used. Topic modeling was conducted on LDA techniques for major projects in 'tourism' and 'livelihood' keyword. Based on this, this study typified the projects that are carried out for the activation of rural areas by topic. As a result of the analysis, it was fount that the topics included in the project were distributed in 11 sub-types(Tourism Promotion, Regional Specialization, Local Festival, Development of Regional Scale, Urban and Rural Exchange, Agricultural Support, Community Forest Management, Improve the Settlement Environment, General Welfare Service, Low Class Support, Others). The characteristics of the rural revitalization projects were examined, and it was confirmed that domestic projects were carried out by tourism-oriented projects. To summarize, the government is making projects to revitalize rural areas through related ministries. Within the structure where the project is spreading to the region, a lot of projects are being carried out. It is understood that the tourism and welfare oriented projects are being carried out in the revitalization project of the domestic rural area. Therefore, in order to achieve the goal of rural revitalization, it is believed that it will be effective to carry out a balanced project to improve the settlement environment of the residents.

• Atmosphere
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• v.29 no.1
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• pp.1-12
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• 2019

For the allergy patient who needs to know the situation about the extent of pollen risk, the National Institute of Meteorological Sciences developed a pollen forecasting system based on the Community Multiscale Air Quality Modeling (CMAQ). In the old system, pollen emission from the oak was estimated just based on the airborne concentration and meteorology factors, resulted in high uncertainty. For improving the quality of current pollen forecasting system, therefore the estimation of pollen emission is now corrected based on the observation of pollen emission at the oak forest to better reflect the real emission pattern. In this study, the performance of the previous (NIMS2014) and current (NIMS2016) model system was compared using observed oak pollen concentration. Daily pollen concentrations and emissions were simulated in pollen season 2016 and accuracy of onset and end of pollen season were evaluated. In the NIMS2014 model, pollen season was longer than actual pollen season; The simulated pollen season started 6 days earlier and finished 13.25 days later than the actual pollen season. The NIMS2016 model, however, the simulated pollen season started only 1.83 days later, and finished 0.25 days later than the actual pollen season, showing the improvement to predict the temporal range of pollen events. Also, the NIMS2016 model shows better performance for the prediction of pollen concentration, while there is a still large uncertainty to capture the maximum pollen concentration at the target site. Continuous efforts to correct these problems will be required in the future.

• Proceedings of the National Institute of Ecology of the Republic of Korea
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• v.2 no.1
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• pp.70-75
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• 2021

The present study aimed to clarify flora living at the area of Restoration Center for Endangered Species in Yeongyang, Gyeongbuk Province. In May, August, and September 2019 and in May and July 2020, all of vascular plants were recorded, and endangered, Korea endemic, and exotic plant species were further identified. The study site contained a total of 418 floral taxa (98 families, 261 genera, 384 species, 4 subspecies, 27 variations, and 3 formations), in which Magnoliophyta accounted for larger proportion (95.2%) than Pteridophyta (3.6%) and Pinophyta (1.2%). In addition, 1 endangered (Cypripedium macranthos Sw.) and 5 Korea endemic species (Aconitum pseudolaeve Nakai, Eleutherococcus divaricatus var. chiisanensis [Nakai] C.H. Kim & B.-Y. Sun, Lonicera subsessilis Rehder, Paulownia coreana Uyeki, and Weigela subsessilis [Nakai] L.H. Bailey) were detected. The number of exotic species was 33, consisting of 4 invasive-exotic, 4 potentially invasive-exotic, and 25 non-invasive species. Compared to a previous assessment before the establishment of the center (in 2014), there were increases in total floral taxa (from 361 to 418), endangered species (from 0 to 1), and exotic species (from 26 to 33). These results possibly reflect temporal changes in floral community, which should be confirmed through subsequent long term monitoring.

• Journal of Ecology and Environment
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• v.45 no.1
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• pp.62-71
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• 2021

The climate is changing rapidly, and this may pose a major threat to global biodiversity. One of the most distinctive consequences of climate change is the poleward and/or upward shift of species distribution ranges associated with increasing temperatures, resulting in a change of species composition and community structure in the forest ecosystems. The Baekdudaegan mountain range connects most forests from the lowland to the subalpine zone in South Korea and is therefore recognized as one of the most important biodiversity hotspots. This study was conducted to understand the distribution range of vascular plants along elevational gradients through field surveys in the six national parks of the Baekdudaegan mountain range. We identified the upper and lower distribution limits of a total of 873 taxa of vascular plants with 117 families, 418 genera, 793 species, 14 subspecies, 62 varieties, two forms, and two hybrids. A total of 12 conifers were recorded along the elevational gradient. The distribution ranges of Abies koreana, Picea jezoensis, Pinus pumila, and Thuja koraiensis were limited to over 1000 m above sea level. We also identified 21 broad-leaved trees in the subalpine zone. A total of 45 Korean endemic plant species were observed, and of these, 15 taxa (including Aconitum chiisanense and Hanabusaya asiatica) showed a narrow distribution range in the subalpine zone. Our study provides valuable information on the current elevational distribution ranges of vascular plants in the six national parks of South Korea, which could serve as a baseline for vertical shifts under future climate change.

• Smart Structures and Systems
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• v.29 no.1
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• pp.93-103
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• 2022

With the wider availability of sensor technology through easily affordable sensor devices, several Structural Health Monitoring (SHM) systems are deployed to monitor vital civil infrastructure. The continuous monitoring provides valuable information about the health of the structure that can help provide a decision support system for retrofits and other structural modifications. However, when the sensors are exposed to harsh environmental conditions, the data measured by the SHM systems tend to be affected by multiple anomalies caused by faulty or broken sensors. Given a deluge of high-dimensional data collected continuously over time, research into using machine learning methods to detect anomalies are a topic of great interest to the SHM community. This paper contributes to this effort by proposing a relatively new time series representation named "Shapelet Transform" in combination with a Random Forest classifier to autonomously identify anomalies in SHM data. The shapelet transform is a unique time series representation based solely on the shape of the time series data. Considering the individual characteristics unique to every anomaly, the application of this transform yields a new shape-based feature representation that can be combined with any standard machine learning algorithm to detect anomalous data with no manual intervention. For the present study, the anomaly detection framework consists of three steps: identifying unique shapes from anomalous data, using these shapes to transform the SHM data into a local-shape space and training machine learning algorithms on this transformed data to identify anomalies. The efficacy of this method is demonstrated by the identification of anomalies in acceleration data from an SHM system installed on a long-span bridge in China. The results show that multiple data anomalies in SHM data can be automatically detected with high accuracy using the proposed method.

• Journal of The Korean Society of Agricultural Engineers
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• v.64 no.5
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• pp.53-65
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• 2022

Most of the reservoirs managed by the city and county are small and it is difficult to respond to climate change because the drainage area is small and the inflow increases rapidly when a heavy rain occurs. In this study, the current status of reservoirs managed by city and county in Gyeonggi-do was reviewed and flood vulnerability due to climate change was analyzed. In order to analyze the impact of climate change, CMIP6-based future climate scenario provided by IPCC was used, and future rainfall data was established through downscaling of climate scenario (SSP8-8.5). The flood vulnerability of reservoirs due to climate change was evaluated using the concept provided by the IPCC. The future annual precipitation at six weather stations appeared a gradual increase and the fluctuation range of the annual precipitation was also found to increase. As a result of calculating the flood vulnerability index, it was analyzed that the flood vulnerability was the largest in the 2055s period and the lowest in the 2025s period. In the past period (2000s), the number of D and E grade reservoirs was 58, but it was found to increase to 107 in the 2055s period. In 2085s, there were 17 E grade reservoirs, which was more than in the past. Therefore, it is necessary to take measures against the increasing risk of flooding in the future.

• Pediatric Gastroenterology, Hepatology & Nutrition
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• v.25 no.5
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• pp.376-386
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• 2022

Functional gastrointestinal disorders (FGIDs) are classified as a combination of persistent gastrointestinal symptoms. The Rome IV criteria can elucidate several factors in the pathogenesis of FGIDs. The frequency of FGIDs can differ between clinical and nonclinical settings and between geographic regions. To determine the global prevalence of FGIDs in neonates and toddlers according to the Rome IV criteria. We included cohort and descriptive observational studies reporting the prevalence of FGIDs according to the Rome IV criteria in children aged 0-48 months. We searched the Medline, Embase, Lilacs, and CENTRAL databases from May 2016 to the present day. Furthermore, unpublished literature was searched to supplement this information. The Strengthening the Reporting of Observational Studies in Epidemiology statement was used to evaluate the risk of bias. A meta-analysis of the proportions was performed using MetaProp in R. The results are reported in forest plots. We identified and analyzed 15 studies comprising 48,325 participants. Six studies were conducted in Europe, three in Latin America, two in North America, and four in Asia. Most participants were 12-48 months old (61.0%) and were recruited from the community. The global prevalence of FGIDs was 22.0% (95% confidence interval, 15-31%). The most common disorder was functional constipation (9.0%), followed by infant regurgitation syndrome (8.0%). Its prevalence was higher in the Americas (28.0%). FGIDs, as defined by the Rome IV criteria, are present in 22% of children, and the most common primary disorder is functional constipation. A higher prevalence of FGIDs has been reported in America.

• Proceedings of the National Institute of Ecology of the Republic of Korea
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• v.4 no.1
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• pp.28-42
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• 2023

Seed dispersal supports community structure, maintains genetic connectivity across fragmented landscapes, and influences vegetation assemblages. In the Philippines, only two seed dispersal studies have compared different dispersal agents. We examined the seed dispersal patterns of water, wind, birds, and bats in the Caliraya Watershed, Philippines. We aimed to determine the floral species that were dispersed and how the forest characteristics influenced seed dispersal. By running seed rain traps and drift litter collection from March to June 2022, we analyzed 14,090 seeds in a privately owned study site within the watershed. Water did not exclusively disperse any species and acted as a secondary disperser. Seed density (seeds/trap) was significantly higher for bird-dispersed (n=166) and bat-dispersed (n=145) seeds than for wind-dispersed (n=79) seeds (One-way analysis of variance [ANOVA]: F_2,87=16.21, P<0.0001). Species number (species/trap) was significantly higher for bird-dispersed (n=3.7) and bat-dispersed (n=3.9) seeds than for wind-dispersed (n=0.2) seeds (One-way ANOVA: F_2,87 =16.67, P<0.0001). Birds dispersed more species because they are more diverse and access a wider variety of fruits, unlike bats. Birds and bats target different fruits and provide separate seed dispersal services. Generalized linear model analyses revealed that the number and basal area of fleshy fruit trees most strongly influenced the bird seed dispersal patterns. Therefore, we recommend a three-way approach to restoration efforts in the Caliraya Watershed: (1) ensure the presence of fleshy fruit trees in restoration zones, (2) assist the establishment of mid-successional and wind-dispersed trees, and (3) intensify the conservation efforts for both flora and faunal diversity.

• Journal of The Korean Society of Agricultural Engineers
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• v.65 no.5
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• pp.1-11
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• 2023

Due to climate change and aging of reservoirs, damage to embankment slopes is increasing. However, the safety diagnosis of the reservoir slope is mainly conducted by visual observation, and the time and economic cost are formidable to apply soil mechanical tests and slope stability analysis. Accordingly, this study presented a predicting method for the compaction and strength characteristics of the reservoir embankment soil using a portable static cone penetration test. The predicted items consisted of dry density, cohesion, and internal friction angle, which are the main factors of slope stability analysis. Portable static cone penetration tests were performed at 19 reservoir sites, and prediction equations were constructed from the correlation between penetration resistance data and test results of soil samples. The predicted dry density and strength parameters showed a correlation with test results between R² 0.40 and 0.93, and it was found to replace the test results well when used as input data for slope stability analysis (R² 0.8134 or more, RMSE 0.0320 or less). In addition, the prediction equations for the minimum safety factor of the slope were presented using the penetration resistance and gradient. As a result of comparing the predicted safety factor with the analysis results, R² 0.5125, RMSE 0.0382 in coarse-grained soil, R² 0.4182 and RMSE 0.0628 in fine-grained soil. The results of this study can be used as a way to improve the existing slope safety diagnosis method, and are expected to be used to predict the characteristics of various soils and inspect slopes.

• Journal of Integrative Natural Science
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• v.17 no.3
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• pp.93-103
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• 2024

Mongolia's diverse geographical landscape and harsh climate make it particularly susceptible to various natural disasters, including forest fires, heavy rains, dust storms, and heavy snow. This study aims to explore the relationships between key climatic variables and the frequency of these disasters. We collected monthly data from January 2022 to April 2024, encompassing average temperature, temperature variability (absolute temperature difference), average humidity, and precipitation across the capitals of Mongolia's 21 provinces and the capital city Ulaanbaatar. The data were analyzed using multiple statistical models: Linear Regression, Poisson Regression, and Negative Binomial Regression. Descriptive statistics provided initial insights into the variability and distribution of the climatic variables and disaster occurrences. The models aimed to identify significant predictors and quantify their impact on disaster frequencies. Our approach involved standardizing the predictor variables to ensure comparability and interpretability of the regression coefficients. Our findings indicate that climatic variables significantly affect the frequency of natural disasters. The Negative Binomial Regression model was particularly suitable for our data, which exhibited overdispersion common characteristic in count data such as disaster occurrences. Understanding these relationships is crucial for developing targeted disaster management strategies and policies to mitigate the adverse effects of climate change on Mongolian communities. This research provides valuable insights into how climatic changes impact disaster occurrences, offering a foundation for informed decision-making and policy development to enhance community resilience.